Overview

Dataset statistics

Number of variables25
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.2 KiB
Average record size in memory200.6 B

Variable types

Numeric15
Categorical10

Alerts

etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 11 other fieldsHigh correlation
longueur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur_voiture is highly overall correlated with empattement and 10 other fieldsHigh correlation
hauteur_voiture is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement and 12 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 9 other fieldsHigh correlation
course is highly overall correlated with marque_de_voiture and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur_voiture and 7 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement and 8 other fieldsHigh correlation
marque_de_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
transmission is highly overall correlated with marque_de_voitureHigh correlation
emplacement_moteur is highly overall correlated with empattement and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 3 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur_voiture and 6 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 3 other fieldsHigh correlation
carburant is highly imbalanced (53.9%)Imbalance
emplacement_moteur is highly imbalanced (89.0%)Imbalance
nombre_cylindres is highly imbalanced (57.6%)Imbalance
etat_de_route has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-04-28 12:45:01.239541
Analysis finished2023-04-28 12:45:35.509888
Duration34.27 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-04-28T14:45:35.574419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-04-28T14:45:35.705303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%
Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length11
Median length10
Mean length6.2195122
Min length3

Characters and Unicode

Total characters1275
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Length

2023-04-28T14:45:35.865543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Most occurring characters

ValueCountFrequency (%)
a 154
12.1%
o 152
 
11.9%
s 101
 
7.9%
t 100
 
7.8%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
4.9%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.8%
Other values (15) 381
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1272
99.8%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 378
29.7%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1272
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 378
29.7%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154
12.1%
o 152
 
11.9%
s 101
 
7.9%
t 100
 
7.8%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
4.9%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.8%
Other values (15) 381
29.9%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2023-04-28T14:45:36.019386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:36.188494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2023-04-28T14:45:36.328613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:36.944451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 689
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 689
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
115 
two
90 

Length

Max length4
Median length4
Mean length3.5609756
Min length3

Characters and Unicode

Total characters730
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Length

2023-04-28T14:45:37.069904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:37.221310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Most occurring characters

ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 730
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

type_vehicule
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2023-04-28T14:45:37.348604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:37.517879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

transmission
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2023-04-28T14:45:37.664545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:37.814364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 606
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 606
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2023-04-28T14:45:37.938218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:38.085415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1022
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1022
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

empattement
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.84173
Minimum219.964
Maximum307.086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:38.222778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum219.964
5-th percentile236.2708
Q1240.03
median246.38
Q3260.096
95-th percentile279.4
Maximum307.086
Range87.122
Interquartile range (IQR)20.066

Descriptive statistics

Standard deviation15.29531
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean250.84173
Median Absolute Deviation (MAD)6.858
Skewness1.0502138
Sum51422.554
Variance233.94652
MonotonicityNot monotonic
2023-04-28T14:45:38.386451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240.03 21
 
10.2%
237.998 20
 
9.8%
243.078 13
 
6.3%
245.11 8
 
3.9%
247.142 7
 
3.4%
249.936 7
 
3.4%
264.922 6
 
2.9%
255.016 6
 
2.9%
274.066 6
 
2.9%
250.952 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
219.964 2
 
1.0%
224.536 1
 
0.5%
225.044 2
 
1.0%
227.33 3
 
1.5%
231.902 2
 
1.0%
236.22 1
 
0.5%
236.474 5
 
2.4%
236.982 1
 
0.5%
237.998 20
9.8%
239.522 1
 
0.5%
ValueCountFrequency (%)
307.086 1
 
0.5%
293.624 2
 
1.0%
290.068 4
2.0%
287.02 2
 
1.0%
284.48 1
 
0.5%
279.4 3
1.5%
277.114 5
2.4%
274.32 1
 
0.5%
274.066 6
2.9%
271.018 1
 
0.5%

longueur_voiture
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.08514
Minimum358.394
Maximum528.574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:38.555182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum358.394
5-th percentile399.1356
Q1422.402
median439.928
Q3465.074
95-th percentile498.7544
Maximum528.574
Range170.18
Interquartile range (IQR)42.672

Descriptive statistics

Standard deviation31.336713
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean442.08514
Median Absolute Deviation (MAD)17.526
Skewness0.15595377
Sum90627.454
Variance981.98957
MonotonicityNot monotonic
2023-04-28T14:45:38.731921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399.542 15
 
7.3%
479.552 11
 
5.4%
436.118 7
 
3.4%
474.218 7
 
3.4%
422.402 7
 
3.4%
419.862 6
 
2.9%
451.612 6
 
2.9%
447.548 6
 
2.9%
473.964 6
 
2.9%
436.88 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
358.394 1
 
0.5%
367.284 2
 
1.0%
381 3
 
1.5%
395.986 3
 
1.5%
398.526 1
 
0.5%
399.034 1
 
0.5%
399.542 15
7.3%
401.066 1
 
0.5%
403.098 3
 
1.5%
403.352 1
 
0.5%
ValueCountFrequency (%)
528.574 1
 
0.5%
514.604 2
1.0%
506.984 2
1.0%
505.968 1
 
0.5%
505.206 4
2.0%
500.38 1
 
0.5%
492.252 1
 
0.5%
489.458 3
1.5%
486.918 1
 
0.5%
484.886 2
1.0%

largeur_voiture
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.40582
Minimum153.162
Maximum183.642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:38.909472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum153.162
5-th percentile161.544
Q1162.814
median166.37
Q3169.926
95-th percentile178.9684
Maximum183.642
Range30.48
Interquartile range (IQR)7.112

Descriptive statistics

Standard deviation5.4488178
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean167.40582
Median Absolute Deviation (MAD)3.556
Skewness0.9040035
Sum34318.194
Variance29.689615
MonotonicityNot monotonic
2023-04-28T14:45:39.064947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
162.052 24
 
11.7%
168.91 23
 
11.2%
166.116 15
 
7.3%
161.544 11
 
5.4%
163.576 10
 
4.9%
173.736 10
 
4.9%
162.56 9
 
4.4%
166.37 8
 
3.9%
165.608 7
 
3.4%
163.068 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
153.162 1
 
0.5%
156.972 1
 
0.5%
158.75 1
 
0.5%
161.036 1
 
0.5%
161.544 11
5.4%
162.052 24
11.7%
162.306 3
 
1.5%
162.56 9
 
4.4%
162.814 2
 
1.0%
163.068 6
 
2.9%
ValueCountFrequency (%)
183.642 1
 
0.5%
182.88 1
 
0.5%
182.118 3
1.5%
181.356 3
1.5%
180.086 1
 
0.5%
179.324 1
 
0.5%
179.07 1
 
0.5%
178.562 3
1.5%
176.784 2
1.0%
175.006 4
2.0%

hauteur_voiture
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.46119
Minimum121.412
Maximum151.892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:39.243579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum121.412
5-th percentile126.238
Q1132.08
median137.414
Q3140.97
95-th percentile146.05
Maximum151.892
Range30.48
Interquartile range (IQR)8.89

Descriptive statistics

Standard deviation6.2065458
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean136.46119
Median Absolute Deviation (MAD)4.064
Skewness0.063122732
Sum27974.544
Variance38.521211
MonotonicityNot monotonic
2023-04-28T14:45:39.418040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
129.032 14
 
6.8%
132.08 12
 
5.9%
141.478 12
 
5.9%
137.414 10
 
4.9%
138.43 10
 
4.9%
140.97 9
 
4.4%
144.018 8
 
3.9%
137.922 8
 
3.9%
133.604 7
 
3.4%
142.494 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
121.412 1
 
0.5%
123.952 2
 
1.0%
125.476 2
 
1.0%
125.984 4
 
2.0%
126.238 3
 
1.5%
127.508 6
2.9%
128.27 2
 
1.0%
128.524 5
 
2.4%
129.032 14
6.8%
129.54 1
 
0.5%
ValueCountFrequency (%)
151.892 2
 
1.0%
150.114 3
 
1.5%
149.098 4
2.0%
148.082 1
 
0.5%
146.05 3
 
1.5%
144.018 8
3.9%
143.51 2
 
1.0%
143.002 2
 
1.0%
142.748 3
 
1.5%
142.494 7
3.4%

poids_vehicule
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.1842
Minimum674.9449
Maximum1844.3051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:39.603535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum674.9449
5-th percentile862.27839
Q1972.95484
median1094.9711
Q31331.2925
95-th percentile1588.9328
Maximum1844.3051
Range1169.3602
Interquartile range (IQR)358.33768

Descriptive statistics

Standard deviation236.17637
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean1159.1842
Median Absolute Deviation (MAD)175.08651
Skewness0.68139819
Sum237632.77
Variance55779.28
MonotonicityNot monotonic
2023-04-28T14:45:39.782280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081.81692 4
 
2.0%
869.989456 3
 
1.5%
1031.9218 3
 
1.5%
902.194488 3
 
1.5%
1093.15672 2
 
1.0%
993.820072 2
 
1.0%
1149.85572 2
 
1.0%
918.070208 2
 
1.0%
1094.971088 2
 
1.0%
1844.305072 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
674.944896 1
0.5%
777.003096 1
0.5%
825.083848 1
0.5%
833.248504 1
0.5%
850.031408 2
1.0%
850.938592 2
1.0%
856.835288 1
0.5%
857.28888 1
0.5%
861.8248 1
0.5%
864.09276 1
0.5%
ValueCountFrequency (%)
1844.305072 2
1.0%
1791.6884 1
0.5%
1769.0088 1
0.5%
1710.04184 1
0.5%
1700.97 1
0.5%
1696.43408 1
0.5%
1685.09428 1
0.5%
1671.48652 1
0.5%
1594.37588 1
0.5%
1589.83996 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-28T14:45:39.949028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:40.148612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-28T14:45:40.320891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:40.519470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.6383
Minimum999.6109
Maximum5342.1829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:40.716285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum999.6109
5-th percentile1474.8358
Q11589.5452
median1966.4477
Q32310.576
95-th percentile3297.0773
Maximum5342.1829
Range4342.572
Interquartile range (IQR)721.03082

Descriptive statistics

Standard deviation682.40148
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean2079.6383
Median Absolute Deviation (MAD)376.90247
Skewness1.947655
Sum426325.86
Variance465671.78
MonotonicityNot monotonic
2023-04-28T14:45:40.880405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1999.221808 15
 
7.3%
1507.609888 15
 
7.3%
1589.545208 14
 
6.8%
1605.932272 14
 
6.8%
1769.802912 13
 
6.3%
1474.83576 12
 
5.9%
1802.57704 12
 
5.9%
1786.189976 8
 
3.9%
1966.44768 7
 
3.4%
2310.576024 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
999.610904 1
 
0.5%
1147.09448 3
 
1.5%
1294.578056 1
 
0.5%
1310.96512 1
 
0.5%
1474.83576 12
5.9%
1491.222824 5
 
2.4%
1507.609888 15
7.3%
1589.545208 14
6.8%
1605.932272 14
6.8%
1687.867592 1
 
0.5%
ValueCountFrequency (%)
5342.182864 1
 
0.5%
5047.215712 1
 
0.5%
4981.667456 1
 
0.5%
4227.862512 2
 
1.0%
3834.572976 2
 
1.0%
3424.896376 3
1.5%
3326.573992 1
 
0.5%
3179.090416 3
1.5%
2998.832712 4
2.0%
2966.058584 6
2.9%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-28T14:45:41.047274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:45:41.225676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
90.0%
Decimal Number 80
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 719
90.0%
Common 80
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.575805
Minimum64.516
Maximum100.076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:41.401309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum64.516
5-th percentile75.438
Q180.01
median84.074
Q390.932
95-th percentile96.012
Maximum100.076
Range35.56
Interquartile range (IQR)10.922

Descriptive statistics

Standard deviation6.8794301
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean84.575805
Median Absolute Deviation (MAD)6.604
Skewness0.020156418
Sum17338.04
Variance47.326559
MonotonicityNot monotonic
2023-04-28T14:45:41.556199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
91.948 23
 
11.2%
81.026 20
 
9.8%
80.01 15
 
7.3%
76.962 12
 
5.9%
75.438 12
 
5.9%
87.884 9
 
4.4%
84.074 8
 
3.9%
87.122 8
 
3.9%
96.012 8
 
3.9%
83.058 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
64.516 1
 
0.5%
68.072 1
 
0.5%
73.914 7
3.4%
74.168 1
 
0.5%
75.438 12
5.9%
75.946 1
 
0.5%
76.454 5
2.4%
76.962 12
5.9%
77.47 6
2.9%
78.232 1
 
0.5%
ValueCountFrequency (%)
100.076 2
 
1.0%
96.52 2
 
1.0%
96.012 8
 
3.9%
95.504 1
 
0.5%
94.996 3
 
1.5%
93.98 5
 
2.4%
92.202 2
 
1.0%
91.948 23
11.2%
91.694 1
 
0.5%
91.44 1
 
0.5%

course
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.687532
Minimum52.578
Maximum105.918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:41.723387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum52.578
5-th percentile67.056
Q178.994
median83.566
Q386.614
95-th percentile92.456
Maximum105.918
Range53.34
Interquartile range (IQR)7.62

Descriptive statistics

Standard deviation7.9653641
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean82.687532
Median Absolute Deviation (MAD)3.556
Skewness-0.68970458
Sum16950.944
Variance63.447026
MonotonicityNot monotonic
2023-04-28T14:45:41.885395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
86.36 20
 
9.8%
82.042 14
 
6.8%
80.01 14
 
6.8%
76.962 14
 
6.8%
86.106 13
 
6.3%
67.056 11
 
5.4%
83.566 9
 
4.4%
85.09 9
 
4.4%
87.884 8
 
3.9%
78.994 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
52.578 1
 
0.5%
55.626 2
 
1.0%
59.944 1
 
0.5%
67.056 11
5.4%
68.072 2
 
1.0%
70.104 1
 
0.5%
71.12 2
 
1.0%
72.898 1
 
0.5%
73.66 3
 
1.5%
76.962 14
6.8%
ValueCountFrequency (%)
105.918 2
 
1.0%
99.06 3
 
1.5%
98.044 4
2.0%
92.456 5
2.4%
90.932 6
2.9%
89.916 4
2.0%
89.408 5
2.4%
88.9 6
2.9%
88.138 4
2.0%
87.884 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:42.036200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-04-28T14:45:42.175764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:42.340312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-04-28T14:45:42.508273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:42.644325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-04-28T14:45:42.789054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9584164
Minimum4.8002041
Maximum18.093077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:42.923626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.8002041
5-th percentile6.357027
Q17.8403333
median9.8004167
Q312.379474
95-th percentile14.700625
Maximum18.093077
Range13.292873
Interquartile range (IQR)4.5391404

Descriptive statistics

Standard deviation2.5754031
Coefficient of variation (CV)0.25861573
Kurtosis-0.16972378
Mean9.9584164
Median Absolute Deviation (MAD)2.2129973
Skewness0.56572572
Sum2041.4754
Variance6.6327011
MonotonicityNot monotonic
2023-04-28T14:45:43.075966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
7.587419355 28
13.7%
12.37947368 27
13.2%
9.800416667 22
10.7%
8.711481481 14
 
6.8%
13.83588235 13
 
6.3%
9.046538462 12
 
5.9%
10.22652174 12
 
5.9%
11.20047619 8
 
3.9%
9.4084 8
 
3.9%
7.840333333 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
4.800204082 1
 
0.5%
5.004468085 1
 
0.5%
5.226888889 1
 
0.5%
6.189736842 7
3.4%
6.357027027 6
2.9%
6.533611111 1
 
0.5%
6.720285714 1
 
0.5%
6.917941176 1
 
0.5%
7.127575758 1
 
0.5%
7.3503125 1
 
0.5%
ValueCountFrequency (%)
18.09307692 1
 
0.5%
16.80071429 2
 
1.0%
15.68066667 3
 
1.5%
14.700625 6
 
2.9%
13.83588235 13
6.3%
13.06722222 3
 
1.5%
12.37947368 27
13.2%
11.7605 3
 
1.5%
11.20047619 8
 
3.9%
10.69136364 4
 
2.0%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0415271
Minimum4.3557407
Maximum14.700625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:43.235079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.3557407
5-th percentile5.4960476
Q16.9179412
median7.8403333
Q39.4084
95-th percentile10.691364
Maximum14.700625
Range10.344884
Interquartile range (IQR)2.4904588

Descriptive statistics

Standard deviation1.8514355
Coefficient of variation (CV)0.23023431
Kurtosis1.1464252
Mean8.0415271
Median Absolute Deviation (MAD)1.4833063
Skewness0.82557301
Sum1648.5131
Variance3.4278133
MonotonicityNot monotonic
2023-04-28T14:45:43.386651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.4084 19
 
9.3%
6.189736842 17
 
8.3%
9.800416667 17
 
8.3%
7.840333333 16
 
7.8%
7.3503125 16
 
7.8%
6.917941176 14
 
6.8%
6.357027027 13
 
6.3%
8.400357143 13
 
6.3%
8.110689655 10
 
4.9%
7.127575758 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
4.355740741 1
 
0.5%
4.437924528 1
 
0.5%
4.7042 1
 
0.5%
5.004468085 2
 
1.0%
5.11326087 2
 
1.0%
5.47 4
 
2.0%
5.600238095 3
 
1.5%
5.736829268 3
 
1.5%
6.031025641 2
 
1.0%
6.189736842 17
8.3%
ValueCountFrequency (%)
14.700625 2
 
1.0%
13.83588235 1
 
0.5%
13.06722222 2
 
1.0%
12.37947368 2
 
1.0%
11.7605 2
 
1.0%
10.69136364 8
3.9%
10.22652174 7
 
3.4%
9.800416667 17
8.3%
9.4084 19
9.3%
9.046538462 3
 
1.5%

prix
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.711
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T14:45:43.551006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.8523
Coefficient of variation (CV)0.60171925
Kurtosis3.0516479
Mean13276.711
Median Absolute Deviation (MAD)3306
Skewness1.7776782
Sum2721725.7
Variance63821762
MonotonicityNot monotonic
2023-04-28T14:45:43.719414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-04-28T14:45:32.664757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:03.614179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.550100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.516493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.781049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.837893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.870491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.830790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.008613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.074391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.988632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.031126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.316634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.278841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.495629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.806701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:03.738324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.669927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.649051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.919172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.994843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.987779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.214137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.144189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.198542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.120956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.150539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.451660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.419587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.652237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.931493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:03.855520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.786553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.778080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.043531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.119506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.107119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.327607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.279815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.315271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.244636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.269910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.572421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.585710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.799571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.080940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:03.999155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.922479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.920971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.183183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.258116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.242025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.471648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.429307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.447277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.390091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.404882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.705975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.746568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.944081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.220530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.133987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.063468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.058380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.319438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.397074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.376174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.597522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.566277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.577179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.529559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.535190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.836408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.889560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.087110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.367586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.262760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.204189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.195439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.461165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.530368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.505524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.724127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.712025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.706695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.663785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:24.663626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.969147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.045037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.236470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.502672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.385108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.328698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.332068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.596913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.664006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.628546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.847213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.849473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.829376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.791317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.101957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.105513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.193036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.387453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.635109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.505466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.441070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.461737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.720857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.785616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.751909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:16.971463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:18.980127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:20.948200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:22.915710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.216973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.226052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.340473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.519213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.781067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.660358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.587736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.822593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.858237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:12.926813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:14.887868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.120328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.126948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.080595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.111801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.389187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.350450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.485892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.664281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:33.920621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.779859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.707042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:08.950423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:10.988106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.057283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.015400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.240765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.256022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.203969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.240124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.520802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.466957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.614406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.790205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:34.066800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:04.914078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.850169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.107866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.133946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.201601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.148600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.369400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.397926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.335589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.378533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.657442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.601304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.753021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:31.938027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:34.199089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.034315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:06.968437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.239607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.293892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.329869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.289019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.494490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.525326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.468211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.501360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.773460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.740771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:29.895360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.067138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:34.329249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.154104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.108850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.367279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.420020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.452966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.426694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.612844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.649457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.585497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.627018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:25.905522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.864859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.024289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.235618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:34.469014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.277815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.249321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.508709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.556853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.586959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.560602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.738523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.793463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.713457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.761110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.042346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:27.991976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.170655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.368647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:34.602569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:05.407728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:07.371915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:09.639466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:11.692084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:13.723611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:15.689799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:17.867085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:19.924564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:21.847073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:23.889920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:26.173683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:28.127457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:30.315578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T14:45:32.508843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-28T14:45:43.900676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
etat_de_routeempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetaille_moteurtaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarque_de_voiturecarburantturbonombre_portestype_vehiculetransmissionemplacement_moteurtype_moteurnombre_cylindressysteme_carburant
etat_de_route1.000-0.538-0.396-0.254-0.523-0.256-0.177-0.170-0.0190.023-0.0100.2820.018-0.053-0.1450.4430.2170.1850.6840.3340.2660.2720.2220.1600.266
empattement-0.5381.0000.9120.8120.6330.7650.6480.5370.227-0.1260.505-0.3120.4930.5390.6820.5070.3410.3100.4450.3340.4170.5680.3530.3160.226
longueur_voiture-0.3960.9121.0000.8880.5250.8900.7830.6390.187-0.1930.661-0.2690.6700.6980.8040.5000.1100.2070.3650.2410.4090.0000.3170.3560.326
largeur_voiture-0.2540.8120.8881.0000.3500.8640.7710.6100.240-0.1460.689-0.1990.6880.7010.8110.5270.2330.3010.3050.1280.4030.1600.3690.5670.246
hauteur_voiture-0.5230.6330.5250.3501.0000.3460.2000.216-0.0180.0000.011-0.2960.0690.1330.2430.4870.2770.2490.5380.4900.3640.2510.3860.3490.298
poids_vehicule-0.2560.7650.8900.8640.3461.0000.8780.7020.163-0.2190.808-0.2360.8130.8340.9090.4940.3050.3750.2740.2300.4560.1000.3270.4820.292
taille_moteur-0.1770.6480.7830.7710.2000.8781.0000.7010.292-0.2350.817-0.2730.7300.7210.8260.5330.1570.2710.2070.2020.4690.6190.5270.6420.333
taux_alésage-0.1700.5370.6390.6100.2160.7020.7011.000-0.083-0.1600.639-0.2980.6090.6150.6440.5190.1740.3470.1230.1260.4490.3470.3930.3430.339
course-0.0190.2270.1870.240-0.0180.1630.292-0.0831.000-0.0700.130-0.0740.0300.0300.1110.5800.3750.2690.1250.1490.3420.6150.4050.2390.303
taux_compression0.023-0.126-0.193-0.1460.000-0.219-0.235-0.160-0.0701.000-0.353-0.022-0.479-0.445-0.1740.4930.9930.5540.1860.0480.1140.0000.3380.5210.518
chevaux-0.0100.5050.6610.6890.0110.8080.8170.6390.130-0.3531.0000.1130.9110.8860.8550.4570.2190.3430.1710.1890.4020.8430.5140.5640.317
tour_moteur0.282-0.312-0.269-0.199-0.296-0.236-0.273-0.298-0.074-0.0220.1131.0000.1310.057-0.0660.4700.5940.3110.2440.0740.2420.4480.3590.2830.363
consommation_ville0.0180.4930.6700.6880.0690.8130.7300.6090.030-0.4790.9110.1311.0000.9680.8290.4000.2990.1910.1070.0810.3890.3630.3340.4980.316
consommation_autoroute-0.0530.5390.6980.7010.1330.8340.7210.6150.030-0.4450.8860.0570.9681.0000.8230.3790.3610.3020.1540.1790.4370.2330.3420.5150.336
prix-0.1450.6820.8040.8110.2430.9090.8260.6440.111-0.1740.855-0.0660.8290.8231.0000.3810.3380.4070.0000.2290.4510.4510.2880.4290.290
marque_de_voiture0.4430.5070.5000.5270.4870.4940.5330.5190.5800.4930.4570.4700.4000.3790.3811.0000.3700.4100.2980.3170.6030.7030.6290.5440.510
carburant0.2170.3410.1100.2330.2770.3050.1570.1740.3750.9930.2190.5940.2990.3610.3380.3701.0000.3740.1610.1730.0880.0000.2500.1550.985
turbo0.1850.3100.2070.3010.2490.3750.2710.3470.2690.5540.3430.3110.1910.3020.4070.4100.3741.0000.0000.0000.1180.0000.1500.1960.610
nombre_portes0.6840.4450.3650.3050.5380.2740.2070.1230.1250.1860.1710.2440.1070.1540.0000.2980.1610.0001.0000.7410.0500.0670.2000.1340.245
type_vehicule0.3340.3340.2410.1280.4900.2300.2020.1260.1490.0480.1890.0740.0810.1790.2290.3170.1730.0000.7411.0000.2140.4380.1320.0680.144
transmission0.2660.4170.4090.4030.3640.4560.4690.4490.3420.1140.4020.2420.3890.4370.4510.6030.0880.1180.0500.2141.0000.1240.4250.3360.387
emplacement_moteur0.2720.5680.0000.1600.2510.1000.6190.3470.6150.0000.8430.4480.3630.2330.4510.7030.0000.0000.0670.4380.1241.0000.3990.2880.000
type_moteur0.2220.3530.3170.3690.3860.3270.5270.3930.4050.3380.5140.3590.3340.3420.2880.6290.2500.1500.2000.1320.4250.3991.0000.5460.377
nombre_cylindres0.1600.3160.3560.5670.3490.4820.6420.3430.2390.5210.5640.2830.4980.5150.4290.5440.1550.1960.1340.0680.3360.2880.5461.0000.373
systeme_carburant0.2660.2260.3260.2460.2980.2920.3330.3390.3030.5180.3170.3630.3160.3360.2900.5100.9850.6100.2450.1440.3870.0000.3770.3731.000

Missing values

2023-04-28T14:45:34.861340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-28T14:45:35.324425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

etat_de_routemarque_de_voiturecarburantturbonombre_portestype_vehiculetransmissionemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprix
03alfa-romerogasstdtwoconvertiblerwdfront225.044428.752162.814123.9521155.752416dohcfour2130.318320mpfi88.13868.0729.0111500011.2004768.71148113495.000
13alfa-romerogasstdtwoconvertiblerwdfront225.044428.752162.814123.9521155.752416dohcfour2130.318320mpfi88.13868.0729.0111500011.2004768.71148116500.000
21alfa-romerogasstdtwohatchbackrwdfront240.030434.848166.370133.0961280.490216ohcvsix2490.833728mpfi68.07288.1389.0154500012.3794749.04653816500.000
32audigasstdfoursedanfwdfront253.492448.564168.148137.9221060.044504ohcfour1786.189976mpfi81.02686.36010.010255009.8004177.84033313950.000
42audigasstdfoursedan4wdfront252.476448.564168.656137.9221280.943808ohcfive2228.640704mpfi81.02686.3608.0115550013.06722210.69136417450.000
52audigasstdtwosedanfwdfront253.492450.342168.402134.8741137.155144ohcfive2228.640704mpfi81.02686.3608.5110550012.3794749.40840015250.000
61audigasstdfoursedanfwdfront268.732489.458181.356141.4781290.015648ohcfive2228.640704mpfi81.02686.3608.5110550012.3794749.40840017710.000
71audigasstdfourwagonfwdfront268.732489.458181.356141.4781339.910768ohcfive2228.640704mpfi81.02686.3608.5110550012.3794749.40840018920.000
81audigasturbofoursedanfwdfront268.732489.458181.356141.9861399.784912ohcfive2146.705384mpfi79.50286.3608.3140550013.83588211.76050023875.000
90audigasturbotwohatchback4wdfront252.730452.628172.466132.0801384.816376ohcfive2146.705384mpfi79.50286.3607.0160550014.70062510.69136417859.167
etat_de_routemarque_de_voiturecarburantturbonombre_portestype_vehiculetransmissionemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprix
195-1volvogasstdfourwagonrwdfront264.922479.552170.688146.0501376.198128ohcfour2310.576024mpfi96.01280.0109.5114540010.2265228.40035713415.0
196-2volvogasstdfoursedanrwdfront264.922479.552170.688142.7481331.292520ohcfour2310.576024mpfi96.01280.0109.511454009.8004178.40035715985.0
197-1volvogasstdfourwagonrwdfront264.922479.552170.688146.0501379.826864ohcfour2310.576024mpfi96.01280.0109.511454009.8004178.40035716515.0
198-2volvogasturbofoursedanrwdfront264.922479.552170.688142.7481381.187640ohcfour2130.318320mpfi91.94880.0107.5162510013.83588210.69136418420.0
199-1volvogasturbofourwagonrwdfront264.922479.552170.688146.0501431.989944ohcfour2130.318320mpfi91.94880.0107.5162510013.83588210.69136418950.0
200-1volvogasstdfoursedanrwdfront277.114479.552175.006140.9701339.003584ohcfour2310.576024mpfi96.01280.0109.5114540010.2265228.40035716845.0
201-1volvogasturbofoursedanrwdfront277.114479.552174.752140.9701383.002008ohcfour2310.576024mpfi96.01280.0108.7160530012.3794749.40840019045.0
202-1volvogasstdfoursedanrwdfront277.114479.552175.006140.9701366.219104ohcvsix2834.962072mpfi90.93272.8988.8134550013.06722210.22652221485.0
203-1volvodieselturbofoursedanrwdfront277.114479.552175.006140.9701459.205464ohcsix2376.124280idi76.45486.36023.010648009.0465388.71148122470.0
204-1volvogasturbofoursedanrwdfront277.114479.552175.006140.9701388.898704ohcfour2310.576024mpfi96.01280.0109.5114540012.3794749.40840022625.0